Probabilistic Reasoning and Decision Making in Sensory-Motor Systems ******************************************************************************
Both living organisms and robotic systems must face the same central difficulty: How to survive being ignorant? How to use an incomplete and uncertain model of their environment to perceive, infer, decide, learn and act efficiently? Indeed, any model of a real phenomenon is incomplete: there are always some hidden variables, not taken into account in the model, that influence the phenomenon. The effect of these hidden variables is that the model and the phenomenon never behave exactly alike. Uncertainty is the direct and unavoidable consequence of incompleteness. A model may not foresee exactly the future observations of a phenomenon as these observations are biased by the hidden variables. It may neither predict exactly the consequences of its decisions. Probability theory, considered as an alternative to logic to model rational reasoning, is the perfect mathematical framework to face this difficult challenge. Learning is used in a first step to transform incompleteness into uncertainty, inference is then used to reason and take decisions based on the probability distributions constructed by learning. This so-called subjectivist approach to probability allows uncertain reasoning as complex and formal as the ones made using logic with exact knowledge. This book presents twelve different implementations of this approach to very different sensory-motor systems either by programming robots or by modeling living systems. Each of these works summarizes a PhD dissertation defended in different European universities. All these works use Bayesian Programming: a mathematical formalism, which defines in simple mathematical terms the way probability, can be used as an alternative to logic. Bayesian Programming also proposes a programming and modeling methodology as, to respect the mathematical formalism, the programmer should follow always the same steps to build his model. Finally, Bayesian Programming is a common language to understand and compare the different models. This language is used all along this book by all the authors and insures the global coherence of these twelve very different examples. More information : http://emotion.inrialpes.fr/BP/spip.php?article18 How to buy : |
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